2022

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7 enterprise data strategy trends

CIO Business Intelligence

Every enterprise needs a data strategy that clearly defines the technologies, processes, people, and rules needed to safely and securely manage its information assets and practices. As with just about everything in IT, a data strategy must evolve over time to keep pace with evolving technologies, customers, markets, business needs and practices, regulations, and a virtually endless number of other priorities.

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Building Our Applications Using Flutter

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Flutter where F stands for Front- end, L stands for Language, U stands for UI layout, T stands for Time, T stands for Tools, E stands for Enable, and R stands for Rich. In other words, Flutter is a tool used in […]. The post Building Our Applications Using Flutter appeared first on Analytics Vidhya.

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What Can AI-Powered RPA and IA Mean For Businesses?

KDnuggets

RPA and IA have stunned the business world by availing impressive, intelligent automation capabilities for scales of businesses across industries, which we'll know in this blog.

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10 Technical Blogs for Data Scientists to Advance AI/ML Skills

DataRobot Blog

Savvy data scientists are already applying artificial intelligence and machine learning to accelerate the scope and scale of data-driven decisions in strategic organizations. These data science teams are seeing tremendous results—millions of dollars saved, new customers acquired, and new innovations that create a competitive advantage. Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

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Data Speaks for Itself: Data Littering

TDAN

No, this is not a mistyping of data literacy. Yes, like everyone, I am aware of and fully on-board with the growing movement to improve data literacy in the enterprise. What I want to talk about is Data Littering, which is something else entirely. Data Littering is the deliberate act of creating and distributing data […].

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5 Ways Data Analytics Helps Investors Maximize Stock Market Returns

Smart Data Collective

We have previously talked about the reasons that data analytics technology is changing the financial industry. One of the most significant changes has been in the field of stock market investing. Analytics Insight has touched on some of the benefits of using data analytics to make better stock market trades. They point out that value investors are using machine learning technology to anticipate future stock prices.

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DataRobot and Snowflake Healthcare Campaign

DataRobot

Shifting to Proactive Healthcare Delivery with AI. The Case for Change. The UK Government Health and Care Bill sets up Integrated Care Systems (ICSs) as legal entities from July 2022. While ICSs have been operating in shadow-format for a number of years, this long-awaited shift determines that health and care delivery in England is regionally managed and focused around the needs of the local population.

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Systems Thinking and Data Science: a partnership or a competition?

Jen Stirrup

Information is pretty thin stuff, unless mixed with experience. – Clarence Day (1874–1935), American essayist. Why do organizations get stuck with their data? It is such a fundamental question. Often, this problem can be due to the organization concentrating solely on technology and data. However, organizations can be supported by a synergistic approach by integrating systems thinking with the data strategy and technical perspective.

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Is Quantum Computing the Future of Artificial Intelligence?

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Source: Forbes.com Introduction It is not hidden from the audience that quantum computing is the future of data processing. Tech giants like IBM, Google, and Microsoft are all aggressively pursuing quantum computing technology for a good reason. The massive speedups and power savings of quantum […].

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Innovative Applications of Machine Learning in Healthcare Domain

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Nowadays, Machine learning is being used in various areas in the health business, including the development of improved medical processes, the management of patient records and data, and the treatment of chronic diseases. Healthcare firms may use machine learning to meet rising demand, […].

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Entity Resolution Checklist: What to Consider When Evaluating Options

Are you trying to decide which entity resolution capabilities you need? It can be confusing to determine which features are most important for your project. And sometimes key features are overlooked. Get the Entity Resolution Evaluation Checklist to make sure you’ve thought of everything to make your project a success! The list was created by Senzing’s team of leading entity resolution experts, based on their real-world experience.

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Blockchain and Deploying Applications on Docker and Kubernetes

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Niti Ayog, one of the transforming national institutions, has published an article on Blockchain use cases in India. Few questions about Blockchain, why Blockchain, and how we can deploy our applications through the docker and Kubernetes we should know. Objectives We will discuss […].

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Step-by-Step Exploratory Data Analysis (EDA) using Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction to EDA The main objective of this article is to cover the steps involved in Data pre-processing, Feature Engineering, and different stages of Exploratory Data Analysis, which is an essential step in any research analysis. Data pre-processing, Feature Engineering, and EDA are fundamental early […].

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Three R Libraries for Automated EDA

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction With the increasing use of technology, data accumulation is faster than ever due to connected smart devices. These devices continuously collect and transmit data that can be processed, transformed, and stored for later use. This collected data, known as big data, holds valuable […].

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Data Science Minimum: 10 Essential Skills You Need to Know to Start Doing Data Science

KDnuggets

Data science is ever-evolving, so mastering its foundational technical and soft skills will help you be successful in a career as a Data Scientist, as well as pursue advance concepts, such as deep learning and artificial intelligence.

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Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity

Speaker: Nicholas Zeisler, CX Strategist & Fractional CXO

The first step in a successful Customer Experience endeavor (or for that matter, any business proposition) is to find out what’s wrong. If you can’t identify it, you can’t fix it! 💡 That’s where the Voice of the Customer (VoC) comes in. Today, far too many brands do VoC simply because that’s what they think they’re supposed to do; that’s what all their competitors do.

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More Data Science Cheatsheets

KDnuggets

It's time again to look at some data science cheatsheets. Here you can find a short selection of such resources which can cater to different existing levels of knowledge and breadth of topics of interest.

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How To Overcome The Fear of Math and Learn Math For Data Science

KDnuggets

Many aspiring Data Scientists, especially when self-learning, fail to learn the necessary math foundations. These recommendations for learning approaches along with references to valuable resources can help you overcome a personal sense of not being "the math type" or belief that you "always failed in math.".

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We Don’t Need Data Scientists, We Need Data Engineers

KDnuggets

As more people are entering the field of Data Science and more companies are hiring for data-centric roles, what type of jobs are currently in highest demand? There is so much data in the world, and it just keeps flooding in, it now looks like companies are targeting those who can engineer that data more than those who can only model the data.

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How I Got 4 Data Science Offers and Doubled My Income 2 Months After Being Laid Off

KDnuggets

In this blog, I shared my story on getting 4 data science job offers including Airbnb, Lyft and Twitter after being laid off. Any data scientist who was laid off due to the pandemic or who is actively looking for a data science position can find something here to which they can relate.

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The Big Payoff of Application Analytics

Outdated or absent analytics won’t cut it in today’s data-driven applications – not for your end users, your development team, or your business. That’s what drove the five companies in this e-book to change their approach to analytics. Download this e-book to learn about the unique problems each company faced and how they achieved huge returns beyond expectation by embedding analytics into applications.

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How Much Math Do You Need in Data Science?

KDnuggets

There exist so many great computational tools available for Data Scientists to perform their work. However, mathematical skills are still essential in data science and machine learning because these tools will only be black-boxes for which you will not be able to ask core analytical questions without a theoretical foundation.

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Introduction to Pandas for Data Science

KDnuggets

The Pandas library is core to any Data Science work in Python. This introduction will walk you through the basics of data manipulating, and features many of Pandas important features.

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If I Had To Start Learning Data Science Again, How Would I Do It?

KDnuggets

While different ways to learn Data Science for the first time exist, the approach that works for you should be based on how you learn best. One powerful method is to evolve your learning from simple practice into complex foundations, as outlined in this learning path recommended by a physicist who turned into a Data Scientist.

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The Complete Collection of Data Science Books – Part 2

KDnuggets

Read the best books on Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, MLOps, Robotics, IoT, AI Products Management, and Data Science for Executives.

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The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data and AI

Speaker: Aindra Misra, Sr. Staff Product Manager of Data & AI at BILL (Previously PM Lead at Twitter/X)

Embark on a transformation journey into the heart of the data ecosystem! This webinar is your gateway to a deeper comprehension of the foundations that drive the data industry and will equip you with the knowledge needed to navigate the evolving landscape. Delve into the diverse use cases where data analytics plays a pivotal role. We’ll explore how these applications are transforming with the introduction of Gen AI, and discuss the anticipated use cases for 2024 and beyond.

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How to Select Rows and Columns in Pandas Using [ ],loc, iloc,at and.iat

KDnuggets

Subset selection is one of the most frequently performed tasks while manipulating data. Pandas provides different ways to efficiently select subsets of data from your DataFrame.

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7 Techniques to Handle Imbalanced Data

KDnuggets

This blog post introduces seven techniques that are commonly applied in domains like intrusion detection or real-time bidding, because the datasets are often extremely imbalanced.

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Easy Guide To Data Preprocessing In Python

KDnuggets

Preprocessing data for machine learning models is a core general skill for any Data Scientist or Machine Learning Engineer. Follow this guide using Pandas and Scikit-learn to improve your techniques and make sure your data leads to the best possible outcome.

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Frameworks for Approaching the Machine Learning Process

KDnuggets

This post is a summary of 2 distinct frameworks for approaching machine learning tasks, followed by a distilled third. Do they differ considerably (or at all) from each other, or from other such processes available?

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Innovation Systems: Advancing Practices to Create New Value

As technology transforms the global business landscape, companies need to examine and update their internal processes for innovation to keep pace. Ultimately, organizations will have to improve the velocity of innovation by creating repeatable processes that support ideation, exploration, and incubation, essential to capturing an idea’s full value.

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Sparse Matrix Representation in Python

KDnuggets

Leveraging sparse matrix representations for your data when appropriate can spare you memory storage. Have a look at the reasons why, see how to create sparse matrices in with Python, and compare the memory requirements for standard and sparse representations of the same data.

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How to Build a Data Science Enablement Team: A Complete Guide

KDnuggets

A Data Science Enablement Team consists of people from various departments like marketing, sales, product development, etc. They are responsible for providing the necessary tools and resources to help the data scientists do their job more efficiently.

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The ABCs of NLP, From A to Z

KDnuggets

There is no shortage of tools today that can help you through the steps of natural language processing, but if you want to get a handle on the basics this is a good place to start. Read about the ABCs of NLP, all the way from A to Z.

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More Performance Evaluation Metrics for Classification Problems You Should Know

KDnuggets

When building and optimizing your classification model, measuring how accurately it predicts your expected outcome is crucial. However, this metric alone is never the entire story, as it can still offer misleading results. That's where these additional performance evaluations come into play to help tease out more meaning from your model.

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How to Stay Competitive in the Evolving State of Martech

Marketing technology is essential for B2B marketers to stay competitive in a rapidly changing digital landscape — and with 53% of marketers experiencing legacy technology issues and limitations, they’re researching innovations to expand and refine their technology stacks. To help practitioners keep up with the rapidly evolving martech landscape, this special report will discuss: How practitioners are integrating technologies and systems to encourage information-sharing between departments and pr